14,681 research outputs found
Three-dimensional connectivity index for texture recognition
AbstractThis work proposes a new method of texture analysis for grey-level images based on the distribution of connectivity indexes in local neighbourhoods. The connectivity index acts as a measure of homogeneity of textures and its distribution is computed at various local neighbourhood sizes. The resulting descriptors provide an efficient multiscale representation of connectivity at different scales. The method was tested in the classification of UIUC, Outex, and KTH-TIPS2b databases and outperformed several state-of-the-art approaches, including such as LBP, LBP+VAR, MR8, multifractals among others
Learning Discriminative Stein Kernel for SPD Matrices and Its Applications
Stein kernel has recently shown promising performance on classifying images
represented by symmetric positive definite (SPD) matrices. It evaluates the
similarity between two SPD matrices through their eigenvalues. In this paper,
we argue that directly using the original eigenvalues may be problematic
because: i) Eigenvalue estimation becomes biased when the number of samples is
inadequate, which may lead to unreliable kernel evaluation; ii) More
importantly, eigenvalues only reflect the property of an individual SPD matrix.
They are not necessarily optimal for computing Stein kernel when the goal is to
discriminate different sets of SPD matrices. To address the two issues in one
shot, we propose a discriminative Stein kernel, in which an extra parameter
vector is defined to adjust the eigenvalues of the input SPD matrices. The
optimal parameter values are sought by optimizing a proxy of classification
performance. To show the generality of the proposed method, three different
kernel learning criteria that are commonly used in the literature are employed
respectively as a proxy. A comprehensive experimental study is conducted on a
variety of image classification tasks to compare our proposed discriminative
Stein kernel with the original Stein kernel and other commonly used methods for
evaluating the similarity between SPD matrices. The experimental results
demonstrate that, the discriminative Stein kernel can attain greater
discrimination and better align with classification tasks by altering the
eigenvalues. This makes it produce higher classification performance than the
original Stein kernel and other commonly used methods.Comment: 13 page
Grey-matter texture abnormalities and reduced hippocampal volume are distinguishing features of schizophrenia
Neurodevelopmental processes are widely believed to underlie schizophrenia. Analysis of
brain texture from conventional magnetic resonance imaging (MRI) can detect disturbance in
brain cytoarchitecture. We tested the hypothesis that patients with schizophrenia manifest
quantitative differences in brain texture that, alongside discrete volumetric changes, may
serve as an endophenotypic biomarker. Texture analysis (TA) of grey matter distribution and
voxel-based morphometry (VBM) of regional brain volumes were applied to MRI scans of 27
patients with schizophrenia and 24 controls. Texture parameters (uniformity and entropy)
were also used as covariates in VBM analyses to test for correspondence with regional brain
volume. Linear discriminant analysis tested if texture and volumetric data predicted
diagnostic group membership (schizophrenia or control). We found that uniformity and
entropy of grey matter differed significantly between individuals with schizophrenia and
controls at the fine spatial scale (filter width below 2 mm). Within the schizophrenia group,
these texture parameters correlated with volumes of the left hippocampus, right amygdala
and cerebellum. The best predictor of diagnostic group membership was the combination of
fine texture heterogeneity and left hippocampal size. This study highlights the presence of
distributed grey-matter abnormalities in schizophrenia, and their relation to focal structural
abnormality of the hippocampus. The conjunction of these features has potential as a
neuroimaging endophenotype of schizophrenia
From receptive profiles to a metric model of V1
In this work we show how to construct connectivity kernels induced by the
receptive profiles of simple cells of the primary visual cortex (V1). These
kernels are directly defined by the shape of such profiles: this provides a
metric model for the functional architecture of V1, whose global geometry is
determined by the reciprocal interactions between local elements. Our
construction adapts to any bank of filters chosen to represent a set of
receptive profiles, since it does not require any structure on the
parameterization of the family. The connectivity kernel that we define carries
a geometrical structure consistent with the well-known properties of long-range
horizontal connections in V1, and it is compatible with the perceptual rules
synthesized by the concept of association field. These characteristics are
still present when the kernel is constructed from a bank of filters arising
from an unsupervised learning algorithm.Comment: 25 pages, 18 figures. Added acknowledgement
- …